Depth Detection Method and System Using Thereof

ABSTRACT

A depth detection method includes the following steps. First, first and second video data are shot. Next, the first and second video data are compared to obtain initial similarity data including r×c×d initial similarity elements, wherein r, c and d are natural numbers greater than 1. Then, an accumulation operation is performed, with each similarity element serving as a center, according to a reference mask to obtain an iteration parameter. Next, n times of iteration update operations are performed on the initial similarity data according to the iteration parameter to generate updated similarity data. Then, it is judged whether the updated similarity data satisfy a character verification condition. If yes, the updated similarity data is converted into depth distribution data.

This application claims the benefit of Taiwan application Serial No.098143011, filed Dec. 15, 2009, the subject matter of which isincorporated herein by reference.

TECHNICAL FIELD

The disclosure relates in general to a depth detection system, and moreparticularly to a depth detection system for obtaining more reliabledepth data using the motion adjustment reference mask technology.

BACKGROUND

In the modern age, in which the technology is growing with each passingday, the digital content industry including computer motion pictures,digital games, digital learning and mobile applications and services isdeveloped in a flourishing manner. In the existing technology, thestereoscopic image/video has existed, and is expected to enhance theservice quality of the digital content industry.

Generally speaking, the conventional depth data detecting system adoptsthe dual camera technology to shoot the target at left and right viewingangles to obtain the left video data and the right video data, andcalculates the depth data of each corresponding object according tohorizontal offsets between the corresponding left and right video dataof the corresponding objects. Generally speaking, the accuracy of thedepth data significantly affects the quality of the stereoscopic imagedata. Thus, it is an important subject of this field to design a depthdetection system capable of generating the accurate depth data.

SUMMARY

The disclosure is directed to a depth detection system adopting a depthestimation apparatus to estimate similarity data of pixel data betweenleft viewing angle video data and right viewing angle video data; togenerate a converging parameter through a reference mask according tothe similarity data in a selected reference region; and to perform acyclic iteration operation on the similarity data according to theconverging parameter so as to obtain the disparity of each pixel data inthe left/right viewing angle video data. The depth detection system ofthe disclosure further adopts the depth estimation apparatus to verifythe disparity, and selectively adjusts the size of the reference maskaccording to the verified result so as to obtain the disparity of eachpixel data with the higher reliability and to correspondingly generatedepth information. Thus, compared with the conventional depth detectionsystem, the depth detection system of the disclosure generates the depthinformation with the higher reliability.

According to a first aspect of the present disclosure, a depth detectionsystem including a dual camera apparatus, a horizontal calibrationapparatus and a depth estimation apparatus is provided. The dual cameraapparatus shoots first video data and second video data, whichrespectively correspond to a first viewing angle and a second viewingangle. Each of the first and second video data include r×c sets of pixeldata, wherein r and c are natural numbers greater than 1. The horizontalcalibration apparatus performs horizontal calibration on the first andsecond video data, and outputs the first and second video data, whichare horizontally calibrated. The depth estimation apparatus includes asimilarity estimation module, an iteration update module and a controlmodule. The similarity estimation module compares pixel data of thefirst and second video data, provided by the horizontal calibrationapparatus, with each other to obtain initial similarity data, whichinclude r×c initial similarity elements each including d initialsimilarity elements, wherein d is a natural number greater than 1. Theiteration update module selects multiple initial similarity elements toperform an accumulation operation to obtain an iteration parameteraccording to a reference mask with each of the initial similarityelements serving as a center. The iteration update module performs ntimes of iteration update operations on the initial similarity dataaccording to the iteration parameter to generate updated similaritydata, which include r×c update similarity elements each including dsimilarity elements. The control module judges whether each of the r×cupdate similarity elements satisfies a character verification condition.When the r×c update similarity elements satisfy the characterverification condition, the control module converts the r×c updatesimilarity elements into depth distribution data.

According to a second aspect of the present disclosure, a depthdetection method is provided. The method includes the following steps.First, first video data and second video data, which respectivelycorrespond to a first viewing angle and a second viewing angle, areshot. Each of the first and second video data include r×c sets of pixeldata, wherein r and c are natural numbers greater than 1. Next,horizontal calibration is performed on the first and second video data.Then, pixel data of the horizontally calibrated first and second videodata are compared with each other to obtain initial similarity data. Theinitial similarity data include r×c initial similarity elements. Each ofthe r×c initial similarity data include d initial similarity elements,wherein d is a natural number greater than 1. Next, multiple similarityelements are selected according to a reference mask with each of thesimilarity elements serving as a center, and an accumulation operationis performed on the selected similarity elements to obtain an iterationparameter. Then, n times of iteration update operations are performed onthe initial similarity data according to the iteration parameter togenerate r×c update similarity elements each including d similarityelements. Next, it is judged whether each of the r×c update similarityelements satisfies a character verification condition. Then, the r×cupdate similarity elements are converted into depth distribution datawhen the r×c update similarity elements satisfy the characterverification condition.

The disclosure will become apparent from the following detaileddescription of the preferred but non-limiting embodiments. The followingdescription is made with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a depth detection system according toan embodiment of the disclosure.

FIG. 2 is a detailed block diagram showing a depth estimation apparatus14 of FIG. 1.

FIG. 3 is a schematic illustration showing r×c×d initial similarityelements in initial similarity data Dis.

FIG. 4 is a detailed block diagram showing a range estimation apparatus18 of FIG. 1.

FIG. 5 is a flow chart showing a depth detection method according to theembodiment of the disclosure.

FIG. 6 is a partial flow chart showing the depth detection methodaccording to the embodiment of the disclosure.

DESCRIPTION OF EMBODIMENTS

FIG. 1 is a block diagram showing a depth detection system 1 accordingto an embodiment of the disclosure. Referring to FIG. 1, the depthdetection system 1 of this embodiment includes a dual camera apparatus10, a horizontal calibration apparatus 12 and a depth estimationapparatus 14. The dual camera apparatus 10 shoots video data V1 and V2,which respectively correspond to a first viewing angle and a secondviewing angle. For example, the video data V1 and V2 are respectivelythe video data of the left viewing angle and the right viewing angleshot on the same target. The video data V1 and V2 include, for example,r×c sets of pixel data, wherein r and c are natural numbers greater than1.

The horizontal calibration apparatus 12 performs horizontal calibrationon the video data V1 and V2, and provides horizontally calibrated videodata Vhc1 and Vhc2 to the depth estimation apparatus 14.

The depth estimation apparatus 14 generates depth distribution data Ddaccording to the video data Vhc1 and Vhc2. FIG. 2 is a detailed blockdiagram showing the depth estimation apparatus 14 of FIG. 1. Forexample, the depth estimation apparatus 14 includes a similarityestimation module 14 a, an iteration update module 14 b and a controlmodule 14 c.

The similarity estimation module 14 a compares pixel data on the videodata Vhc1 and Vhc2 to obtain initial similarity data Dis. For example,the initial similarity data Dis include r×c initial similarity elementseach including d initial similarity elements, wherein d is a naturalnumber greater than 1. For example, based on each of the r×c sets ofpixel data corresponding to the video data Vhc1 of the left viewingangle, the similarity estimation module 14 a selects a search windowincluding d sets of pixel data on the video data Vhc2 corresponding tothe right viewing angle and compares each set of pixel data in the videodata Vhc1 with the d sets of pixel data in the search window to obtainthe corresponding d initial similarity elements.

In one example embodiment, in respect of the pixel data Vhc1(R,C) of thevideo data Vhc1 corresponding to the position (R,C), the similarityestimation module 14 a defines a corresponding search window with thepixel data Vhc2(R,C), Vhc2(R,C+1), Vhc2(R,C+2), . . . , Vhc2(R,C+d),which are respectively corresponding to the positions (R,C), (R,C+1),(R,C+2), . . . , (R,C+d), of the video data Vhc2, wherein R and C arenatural numbers respectively smaller than or equal to r and c. Thesimilarity estimation module 14 a further compares the pixel dataVhc1(R,C) with each of the sets of pixel data Vhc2(R,C) to Vhc2(R,C+d)in the search window to correspondingly obtain the d initial similarityelements.

For example, each of the d initial similarity elements in each of ther×c initial similarity elements may be represented by the followingequation:

L ₀(x,y,z)=δ(Vhc1,Vhc2,x,y,z)|x=1,2, . . . , r; y=1,2, . . . , c; z=1,2,. . . , d,

wherein the δ function is the similarity function of the image. Becausethe initial similarity data Dis include the r×c initial similarityelements and each initial similarity element includes the d initialsimilarity elements, the r×c×d initial similarity elementsL₀(x,y,z)|x=1, 2, . . . , r; y=1, 2, . . . , c; z=1, 2, . . . , d in theinitial similarity data Dis may be represented by a three-dimensionalsimilarity space, as shown in FIG. 3.

The iteration update module 14 b selects multiple similarity elementsaccording to one reference mask M with each similarity element in thethree-dimensional similarity space serving as a center, and performs anaccumulation operation on the selected similarity elements to obtain aniteration parameter P_(n)(n=0, 1, . . . , N). The iteration updatemodule 14 b further performs N times of iteration update operations onthe initial similarity data Dis according to the iteration parameter togenerate updated similarity data Dus according to the initial similaritydata Dis, wherein N is a natural number greater than 1. Similar to theinitial similarity data Dis, the updated similarity data Dus include r×cinitial similarity elements, wherein each of the r×c update similarityelements includes d update similarity elements.

For example, the iteration update module 14 b performs the iterationupdate operation on the initial similarity data Dis according to theiteration parameter and according to the following function:

L _(n+1)(x,y,z)=L ₀(x,y,z)×P _(n) |n=0,1, . . . , N

The iteration parameter P_(n), relates to the accumulation functionS_(n) (x,y,z). For example, the iteration parameter P_(n), and theaccumulation function S_(n)(x,y,z) respectively satisfy, for example,the following equations:

${S_{n}\left( {x,y,z} \right)} = {\sum\limits_{({r^{\prime},c^{\prime},d^{\prime}})}{L_{n}\left( {{x + r^{\prime}},{y + c^{\prime}},{z + d^{\prime}}} \right)}}$$P_{n} = \left( \frac{S_{n}\left( {x,y,z} \right)}{\sum\limits_{{({r^{''},c^{''},d^{''}})} \in {\phi {({x,y,z})}}}{S_{n}\left( {r^{''},c^{''},d^{''}} \right)}} \right)^{\alpha}$

wherein x+r′, y+c′ and z+d′ represent the reference range selected fromthe three-dimensional similarity space of FIG. 3 using the referencemask M with the size of r′×c′×d′ and with the coordinates (x,y,z)serving as the center on the condition that the similarity elementL₀(x,y,z) corresponding to the pixel data Vhc1(x,y) serves as a centerpoint; α is a constant parameter; the accumulation function S_(n)(x,y,z) represents the accumulation operation of the similarity elementsperformed on the selected reference range; and the function

$\sum\limits_{{({r^{''},c^{''},d^{''}})} \in {\phi {({x,y,z})}}}{S_{n}\left( {r^{''},c^{''},d^{''}} \right)}$

represents the reference summation parameter of the accumulationfunction S_(n) (x,y,z) in another selected reference range φ(x,y,z).

The control module 14 c receives the updated similarity data Dus andjudges whether each of the r×c update similarity elements in the updatedsimilarity data Dus satisfies a character verification condition. In oneexample embodiment, the character verification condition is whether eachof the r×c update similarity elements obviously has one unique updatesimilarity element. For example, the control module 14 c substrates ansummation average thereof from the d update similarity elements of ther×c update similarity elements, and judges whether the obtained value isgreater than a threshold value to judge whether each of the r×c updatesimilarity elements obviously has the unique update similarity element.

When each of the r×c update similarity elements includes one uniqueupdate similarity element, it represents that each of the r×c sets ofpixel data of the video data Vhc1 may be mapped to the r×c sets of pixeldata of the video data Vhc2 through the r×c unique update similarityelements in the updated similarity data Dus. Thus, the control module 14c can obtain the horizontal displacement quantity of each of the r×csets of pixel data of the video data Vhc1, and the horizontaldisplacement quantities indicate the horizontal displacements of each ofthe r×c sets of pixel data of the video data Vhc1 relative to the pixeldata of the video data Vhc2 having the same image content. Based on thecondition that the horizontal distance between the pixel data of thevideo data Vhc1 and Vhc2 relates to the depth of its corresponding imagecontent, the control module 14 c generates the depth distribution dataDd according to the horizontal displacement quantity.

When each of the r×c update similarity elements does not include theunique update similarity element, it represents that each of the r×cupdate similarity elements cannot definitely indicate the correspondingrelationship between the r×c sets of pixel data of the video data Vhc1and Vhc2. Thus, the control module 14 c cannot obtain the horizontaldisplacement quantity, relative to the pixel data of the video dataVhc2, of each of the r×c sets of pixel data of the video data Vhc1 andthe corresponding depth distribution data Dd. In this case, the controlmodule 14 c adjusts the size of the reference mask M to try to refer tomore update similarity elements (i.e. select more sets of pixel data bya larger mask in the video data V1) by enlarging the size of thereference mask M when calculating the accumulation functionS_(n)(x,y,z). Thus, the opportunity of referring to the mask of thevideo data V1 with the texture characteristic can be increased.

Thereafter, the control module 14 c transfers the size (M_size) of thereference mask M back to the iteration update module 14 b to drive theiteration update module 14 b to recalculate the iteration parameteraccording to the adjusted reference mask M, and to regenerate theupdated similarity data according to the recalculated iterationparameter. The control module 14 c further judges whether each of ther×c update similarity elements in the updated similarity data Dussatisfies the character verification condition according to theregenerated updated similarity data Dus. If so, the control module 14 cmay generate the depth distribution data Dd according to the updatedsimilarity data Dus. If not, the control module 14 c again adjusts thesize of the reference mask M and repeats the above-mentioned operation.Thus, the depth detection system 1 according to the embodiment of thedisclosure can obtain the similarity data relating to the video dataVhc1 and Vhc2, which has the higher reliability, by the method ofdynamically adjusting the size of the reference mask M, so that thedepth distribution data Dd with the higher reliability can be obtained.

In one example, the depth detection system 1 according to the embodimentof the disclosure further includes a characteristic analyzing apparatus16 for analyzing the characteristic region in the horizontallycalibrated video data Vhc1 and Vhc2, and a range estimation apparatus 18for estimating the possible depth range of the video data Vhc1 and Vhc2according to the analyzed result of the characteristic region togenerate depth range data Ddr.

More specifically speaking, the characteristic analyzing apparatus 16receives and analyzes the video data Vhc1 and Vhc2 provided by thehorizontal calibration apparatus 12 to obtain characteristic region dataDca1 from the video data Vhc1, and to obtain characteristic region dataDca2 from the video data Vhc2, wherein each of the characteristic regiondata Dca1 and Dca2 includes multiple sets of corresponding minutia pointdata. For example, the characteristic analyzing apparatus 16 obtainsmultiple sets of minutia point data by the object dividing technology toindicate several image content objects in the video data Vhc1 and thusto obtain the characteristic region data Dca1. For example, thecharacteristic region data Dca1 include the video data of the video dataVhc1 for displaying the user's hand (usually having the minimum depth),and the video data of the background region (usually having the maximumdepth) in the video data. The same object dividing technology is alsoapplied to the video data Vhc2 to obtain the characteristic region dataDca2 from the video data Vhc2, wherein the characteristic region dataDca2 include multiple sets of minutia point data corresponding to theminutia point data in the characteristic region data Dca1.

FIG. 4 is a detailed block diagram showing the range estimationapparatus 18 of FIG. 1. Referring to FIG. 4, the depth range estimationapparatus 18 includes, for example, an estimation module 18 a, astatistics module 18 b and an operation module 18 c. The estimationmodule 18 a calculates multiple horizontal displacement quantitiesbetween each minutia point in the characteristic region data Dca1 andeach corresponding minutia point in the characteristic region data Dca2,and thus converts the horizontal displacement quantities into multiplesets of depth data. Similar to the operation of the control module 14 c,the estimation module 18 a obtains the depth data Ddc for indicating itscorresponding estimated depths according to the horizontal displacementquantities between the characteristic region data Dca1 in the video dataVhc1 and the characteristic region data Dca2 in the video data Vhc2.

The statistics module 18 b converts the depth data Ddc into depthstatistics distribution data Ddch, and counts the number of minutiapoints, which may be accumulated on a range of multiple possible depths.For example, the depth statistics distribution data Ddch may berepresented by a statistics histogram to indicate the relationshipbetween the depth value and the number of the minutia points thereon.

The operation module 18 c obtains a minimum depth value and a maximumdepth value from the depth statistics distribution data Ddch accordingto a critical condition, determines the depth range data Ddrcorresponding to the video data Vhc1 and Vhc2 according to the minimumand maximum depth values, and outputs the depth range data Ddr to thedepth estimation apparatus 14. For example, the critical condition isthe critical number of the minutia points corresponding to the samedepth value. When the minimum depth value is searched, the operationmodule 18 c starts the search from the corresponding minimum depth inthe depth statistics distribution data Ddch. Once the number of thecorresponding minutia points is found to be greater than or equal to thecritical number of the minutia points, the operation module 18 c servesas the minimum depth value. Similarly, when the maximum depth value issearched, the operation module 18 c starts the search from thecorresponding maximum depth of the depth statistics distribution dataDdch to find the maximum depth where the number of the correspondingminutia points is greater than or equal to of the critical number of theminutia points.

Thus, the depth estimation apparatus 14 can generate the depthdistribution data Dd based on the depth range data Ddr. For example, thesimilarity estimation module 14 a in the depth estimation apparatus 14can determine the value d (i.e., the size of the search window forsimilarity calculation) according to the depth range data Ddr.

FIG. 5 is a flow chart showing a depth detection method according to theembodiment of the disclosure. First, as shown in step (a), the dualcamera apparatus 10 shoots the video data V1 and V2, which respectivelycorrespond to the first viewing angle (e.g., the left viewing angle) andthe second viewing angle (e.g., the right viewing angle), wherein eachof the video data V1 and V2 includes r×c sets of pixel data. Next, asshown in step (b), the horizontal calibration apparatus 12 performshorizontal calibration on the video data V1 and V2 to providehorizontally calibrated video data Vhc1 and Vhc2.

Then, as shown in step (c), the similarity estimation module 14 a of thedepth estimation apparatus 14 compares the pixel data of the video dataVhc1 and Vhc2 to obtain the initial similarity data Dis, which includer×c×d initial similarity elements. Next, as shown in step (d), theiteration update module 14 b of the depth estimation apparatus 14selects multiple similarity elements according to the reference mask Mwith each similarity element serving as the center, and performs theaccumulation operation on the selected similarity element to obtain theiteration parameter P_(n).

Next, as shown in step (e), the iteration update module 14 b furtherperforms n times of iteration update operations on the initialsimilarity data Dis according to the iteration parameter P_(n) togenerate the r×c update similarity elements (i.e., the r×c×d initialsimilarity elements) according to the r×c initial similarity elements(i.e., the r×c×d initial similarity elements). Then, as shown in step(f), the control module 14 c judges whether each of the r×c updatesimilarity elements satisfies a character verification condition. If so,step (g) is performed so that the control module 14 c converts the r×cupdate similarity elements into the depth distribution data Dd. If not,step (h) is performed so that the control module 14 c adjusts the sizeof the reference mask M and then the steps (d), (e) and (f) are repeatedto repeatedly obtain the iteration parameter P_(n), generate the r×cupdate similarity elements and judge whether each of the r×c updatesimilarity elements satisfies the character verification condition.After the step (f), the step (g) is performed if each of the r×c updatesimilarity elements satisfies the character verification condition, andsteps (h) and (d) to (f) are repeated if each of the r×c updatesimilarity elements does not satisfy the character verificationcondition.

FIG. 6 is a partial flow chart showing the depth detection methodaccording to the embodiment of the disclosure. In one example, themethod further includes, between the steps (b) and (c), the steps (i) to(l) for calculating the depth range data Ddr to speed up the operationof the step (c). As shown in the step (i), the characteristic analyzingapparatus 16 analyzes the horizontally calibrated video data Vhc1 andVhc2 to obtain the characteristic region data Dca1 from the video dataVhc1 and obtain the characteristic region data Dca2 from the video dataVhc2. The characteristic region data Dca1 and Dca2 include severalcorresponding pairs of minutia points. Next, as shown in the step (j),the estimation module 18 a calculates the horizontal displacementquantities between the minutia points of the characteristic region dataDca1 and Dca2, and converts the horizontal displacement quantities intothe depth data Ddc corresponding to each of the minutia points.

Then, as shown in the step (k), the statistics module 18 b converts thedepth data Ddc into the depth statistics distribution data Ddch.Thereafter, as shown in the step (l), the operation module 18 c obtainsthe minimum depth value and the maximum depth value from the depthstatistics distribution data Ddch according to a critical condition, anddetermines the depth range data Ddr corresponding to the video data Vch1and Vch2 according to the minimum and maximum depth values.

The depth detection system according to the embodiment of the disclosureadopts the depth estimation apparatus to estimate the similarity data ofthe pixel data between the left viewing angle video data and the rightviewing angle video data; generates the converging parameter through thereference mask according to the similarity data in a selected referenceregion; and performs the cyclic iteration operation on the similaritydata according to the converging parameter to obtain the disparity ofeach pixel data in the left viewing angle/right viewing angle videodata. The depth detection system according to the embodiment of thedisclosure further adopts the depth estimation apparatus to verify thedisparity, and selectively adjusts the size of the reference maskaccording to the verified result to obtain the disparity of each pixeldata with the higher reliability and the correspondingly generated depthinformation. Thus, compared with the conventional depth detectionsystem, the depth detection system according to the embodiment of thedisclosure generates the depth information with the higher reliability.

In addition, the depth detection system according to the embodiment ofthe disclosure further adopts the characteristic analyzing apparatus andthe range estimation apparatus to obtain the possible depth range forthe left viewing angle video data and the right viewing angle videodata. Thus, the depth detection system according to the embodiment ofthe disclosure further increases the operation speed of obtaining theinitial similarity data and the depth information.

While the disclosure has been described by way of example and in termsof a preferred embodiment, it is to be understood that the disclosure isnot limited thereto. On the contrary, it is intended to cover variousmodifications and similar arrangements and procedures, and the scope ofthe appended claims therefore should be accorded the broadestinterpretation so as to encompass all such modifications and similararrangements and procedures.

1. A depth detection system, comprising: a dual camera apparatus forshooting first video data and second video data, which respectivelycorrespond to a first viewing angle and a second viewing angle, whereineach of the first and second video data comprise r×c sets of pixel data,wherein r and c are natural numbers greater than 1; a horizontalcalibration apparatus for performing horizontal calibration on the firstand second video data, and outputting the first and second video data,which are horizontally calibrated; and a depth estimation apparatus,comprising: a similarity estimation module for comparing pixel data ofthe first and second video data, provided by the horizontal calibrationapparatus, with each other to obtain initial similarity data, whichcomprise r×c sets of initial similarity elements, each comprising dinitial similarity elements, wherein d is a natural number greater than1; an iteration update module for selecting multiple initial similarityelements to perform an accumulation operation to obtain an iterationparameter according to a reference mask with each of the initialsimilarity elements serving as a center, wherein the iteration updatemodule performs n times of iteration update operations on the initialsimilarity data according to the iteration parameter to generate updatedsimilarity data, which comprise r×c sets of update similarity elements,each comprising d similarity elements; and a control module for judgingwhether each of the r×c sets of update similarity elements satisfies acharacter verification condition; wherein when the r×c sets of updatesimilarity elements satisfy the character verification condition, thecontrol module converts the r×c sets of update similarity elements intodepth distribution data.
 2. The system according to claim 1, whereinwhen the r×c sets of update similarity elements do not satisfy thecharacter verification condition, the control module adjusts a size ofthe reference mask, and drives the iteration update module to againcalculate the iteration parameter according to the adjusted referencemask and again generate the updated similarity data comprising the r×csets of update similarity elements according to the recalculatediteration parameter.
 3. The system according to claim 2, wherein: thecontrol module further judges whether the re-obtained r×c sets of updatesimilarity elements satisfy the character verification condition; andwhen each of the re-obtained r×c sets of update similarity elementssatisfies the character verification condition, the control moduleconverts the re-obtained r×c sets of update similarity elements into thedepth distribution data.
 4. The system according to claim 3, whereinwhen each of the re-obtained r×c sets of update similarity elementsstill does not satisfy the character verification condition, the controlmodule again adjusts the size of the reference mask, and drives theiteration update module to again calculate the iteration parameteraccording to the adjusted reference mask, and again generate the updatedsimilarity data comprising the r×c sets of update similarity elementsaccording to the recalculated iteration parameter.
 5. The systemaccording to claim 1, further comprising: a characteristic analyzingapparatus for receiving and analyzing the first and second video dataprovided by the horizontal calibration apparatus to obtain firstcharacteristic region data and second characteristic region dataaccording to the first and second video data, respectively, wherein thefirst characteristic region data respectively correspond to the secondcharacteristic region data; and a depth range estimation apparatus,which comprises: an estimation module for receiving and calculating ahorizontal displacement quantity between the first characteristic regiondata and the corresponding second characteristic region data, andconverting the horizontal displacement quantity into depth data; astatistics module for converting the depth data into a set of depthstatistics distribution data; and an operation module for obtaining aminimum depth value and a maximum depth value from the set of depthstatistics distribution data according to a first critical condition,and determining depth range data corresponding to the first and secondvideo data according to the minimum and maximum depth values; whereinthe depth estimation apparatus determines the depth distribution databased on the depth range data.
 6. The system according to claim 5,wherein the operation module obtains the minimum and maximum depthvalues from the depth statistics distribution data according to acritical number.
 7. A depth detection method, comprising the steps of:shooting first video data and second video data, which respectivelycorrespond to a first viewing angle and a second viewing angle, whereineach of the first and second video data comprise r×c sets of pixel data,wherein r and c are natural numbers greater than 1; performinghorizontal calibration on the first and second video data; comparingpixel data of the horizontally calibrated first and second video datawith each other to obtain initial similarity data, wherein the initialsimilarity data comprise r×c sets of initial similarity elements, andeach of the r×c sets of initial similarity data comprise d initialsimilarity elements, wherein d is a natural number greater than 1;selecting multiple similarity elements according to a reference maskwith each of the similarity elements serving as a center, and performingan accumulation operation on the selected similarity elements to obtainan iteration parameter; performing n times of iteration updateoperations on the initial similarity data according to the iterationparameter to generate r×c sets of update similarity elements, eachcomprising d similarity elements; judging whether each of the r×c setsof update similarity elements satisfies a character verificationcondition; and converting the r×c sets of update similarity elementsinto depth distribution data when the r×c sets of update similarityelements satisfy the character verification condition.
 8. The methodaccording to claim 7, further comprising the step of: adjusting a sizeof the reference mask when the r×c sets of update similarity elements donot satisfy the character verification condition; wherein after the sizeof the reference mask is adjusted, the steps of obtaining the iterationparameter, generating the r×c sets of update similarity elements andjudging whether each of the r×c sets of update similarity elementssatisfies the character verification condition are repeated.
 9. Themethod according to claim 8, further comprising the step of: convertingthe re-obtained r×c sets of update similarity elements into the depthdistribution data when each of the re-obtained r×c sets of updatesimilarity elements satisfies the character verification condition. 10.The method according to claim 9, further comprising the step of:repeating the step of adjusting the size of the reference mask when eachof the re-obtained r×c sets of update similarity elements still does notsatisfy the character verification condition; wherein after the size ofthe reference mask is adjusted, the steps of obtaining the iterationparameter, generating the r×c sets of update similarity elements andjudging whether each of the r×c sets of update similarity elementssatisfies the character verification condition are repeated.
 11. Themethod according to claim 7, further comprising, before the step ofobtaining the initial similarity data, the steps of: analyzing thehorizontally calibrated first and second video data to obtain firstcharacteristic region data from the first video data and to obtainsecond characteristic region data from the second video data, whereinthe first characteristic region data correspond to the secondcharacteristic region data; calculating a horizontal displacementquantity between the first characteristic region data and thecorresponding second characteristic region data, and converting thehorizontal displacement quantity into depth data; converting the depthdata into one set of depth statistics distribution data; and obtaining aminimum depth value and a maximum depth value from the set of depthstatistics distribution data according to a first critical condition,and determining depth range data corresponding to the first and secondvideo data according to the minimum and maximum depth values.
 12. Themethod according to claim 11, wherein an operation module obtains theminimum and maximum depth values from the depth statistics distributiondata according to a critical number.